AutoGluon Predictors¶
Example (Predictor for tabular data):
Import TabularDataset and TabularPredictor:
>>> from autogluon.tabular import TabularDataset, TabularPredictor
Load a tabular dataset:
>>> train_data = TabularDataset("https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv")
Fit classification models predicting the “class” column:
>>> predictor = TabularPredictor(label="class").fit(train_data)
Load test data:
>>> test_data = TabularDataset("https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv")
Evaluate predictions on test data:
>>> leaderboard = predictor.leaderboard(test_data)
Example (Deep learning predictor for image, text and multimodal data):
Import MultiModalPredictor:
>>> from autogluon.multimodal import MultiModalPredictor
>>> from datasets import load_dataset
Load a multimodal data table:
>>> train_data = load_dataset("glue", 'mrpc')['train'].to_pandas().drop('idx', axis=1)
Fit classification models predicting the “class” column:
>>> predictor = MultiModalPredictor(label="label").fit(train_data)
Load test data:
>>> test_data = load_dataset("glue", 'mrpc')['validation'].to_pandas().drop('idx', axis=1)
Evaluate predictions on test data:
>>> score = predictor.evaluate(test_data)
Predictors¶
Predictors built into AutoGluon such that a single call to fit() can produce high-quality trained models for tabular, image, or text data. For other applications, you can still use AutoGluon to tune the hyperparameters of your own custom models and training scripts.